Plos One
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Here we show that bortezomib induces effective proteasome inhibition and accumulation of poly-ubiquitinated proteins in diffuse large B-cell lymphoma (DLBCL) cells. This leads to induction of endoplasmic reticulum (ER) stress as demonstrated by accumulation of the protein CHOP, as well as autophagy, as demonstrated by accumulation of LC3-II proteins. Our data suggest that recruitment of both ubiquitinated proteins and LC3-II by p62 directs ubiquitinated proteins, including I-κBα, to the autophagosome. ⋯ Importantly, the combination of proteasome and autophagy inhibitors showed synergy in killing DLBCL cells. In summary, bortezomib-induced autophagy confers relative DLBCL cell drug resistance by eliminating I-κBα. Inhibition of both autophagy and the proteasome has great potential to kill apoptosis-resistant lymphoma cells.
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Randomized Controlled Trial
PTEN as a prognostic and predictive marker in postoperative radiotherapy for squamous cell cancer of the head and neck.
Tumor suppressor PTEN is known to control a variety of processes related to cell survival, proliferation, and growth. PTEN expression is considered as a prognostic factor in some human neoplasms like breast, prostate, and thyroid cancer. ⋯ These results suggest that PTEN may serve as a potent prognostic and predictive marker in postoperative radiotherapy for high-risk squamous cell cancer of the head and neck.
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Since September 2005, the International Committee of Medical Journal Editors (ICMJE) has required that randomised controlled trials (RCTs) are prospectively registered in a publicly accessible database. After registration, a trial registration number (TRN) is assigned to each RCT, which should make it easier to identify future publications and cross-check published results with associated registry entries, as long as the unique identification number is reported in the article. ⋯ Our results show that further promotion and implementation of trial registration and accurate reporting of TRN is still needed. This might be helped by inclusion of the TRN as an item on the CONSORT checklist.
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Genetic predisposition to multiple sclerosis (MS) in Sardinia (Italy) has been associated with five DRB1*-DQB1* haplotypes of the human leukocyte antigen (HLA). Given the complexity of these associations, an in-depth re-analysis was performed with the specific aims of confirming the haplotype associations; establishing the independence of the associated haplotypes; and assessing patients' genotypic risk of developing MS. ⋯ These findings show that the association of specific, independent DRB1*-DQB1* haplotypes confers susceptibility or resistance to MS in the MS-prone Sardinian population. The data also supports a functional role for specific residues of the DRB1 and DQB1 proteins in predisposing patients to MS.
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Comparative Study
A comparison of administrative and physiologic predictive models in determining risk adjusted mortality rates in critically ill patients.
Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients. ⋯ In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models.